Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty

Abstract

Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. The approach forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruptions. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a flight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop a solution procedure based on look-ahead approximation and sample average approximation, which enables the model’s implementation in short computational times. Experimental results show that leveraging even partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1%–2%, as compared with a myopic baseline approach based on realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, flight cancellations, and aircraft swaps.

Electric Taxiing with Disruption Management: Assignment of Electric Towing Vehicles to Aircraft

“…The proposed model reduces the disruption recovery costs by 1 to 4% compared to a baseline flight planning model. In later work, the authors show that using probabilistic forecasts reduces expected recovery costs by 1 to 2% more than using only real-time disruptions [20].…”

It's not simply luck: The impact of network strategy, schedule padding, and operational improvements on domestic on‐time performance in the US airline industry

“…Also, there are several studies in the literature, which focus on real-time disruption management in airline operations to minimize delays. Lee et al (2020) propose a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions, and in anticipation of future disruptions. Chen and Solak (2020) develop a stochastic dynamic programming model to improve the efficiency of surface traffic management and departure operations.…”

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“…Contemporary research in traffic flow management focuses on the recovery approach, which is a reactive approach that handles delays when they arise [18]. Several congestion control methods, such as runway configuration switch [19], ground delay programmes, point-of-merge decision and aeronautical holding assignment can be applied to resolve the contemporary traffic condition, reduce unnecessary delays and offload congested air routes [20]. Therefore, the TTFP schedule should inherently include a certain degree of solution robustness and network resilience to increase the vulnerability of the disruption of airside operations.…”